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Cause or Trigger? From Philosophy to Causal Modeling

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arXiv 2025年

作者： Hlaváčková-Schindler, Kateřina Wöß, Rainer Pecorino, Vera Schindler, Philip Research Group Data Mining and Machine Learning Faculty of Computer Science University of Vienna Vienna Austria Department of Physics and Astronomy University of Catania Catania Italy Faculty of Philosophy University of Vienna Vienna Austria

Not much has been written about the role of triggers in the literature on causal reasoning, causal modeling, or philosophy. In this paper, we focus on describing triggers and causes in the metaphysical sense and on characterizations that differentiate them from each other. We carry out a philosophical analysis of these differences. From this, we formulate a definition that clearly differentiates triggers from causes and can be used for causal reasoning in natural sciences. We propose a mathematical model and the Cause- Trigger algorithm, which, based on given data to observable processes, is able to determine whether a process is a cause or a trigger of an effect. The possibility to distinguish triggers from causes directly from data makes the algorithm a useful tool in natural sciences using observational data, but also for real-world scenarios. For example, knowing the processes that trigger causes of a tropical storm could give politicians time to develop actions such as evacuation the population. Similarly, knowing the triggers of processes that cause global warming could help politicians focus on effective actions. We demonstrate our algorithm on the climatological data of two recent cyclones, Freddy and Zazu. The Cause-Trigger algorithm detects processes that trigger high wind speed in both storms during their cyclogenesis. The findings obtained agree with expert knowledge. © 2025, CC BY.

关键词： Physical properties

Pattern Recognition for Brain-Computer Interface on Disabled Subjects using a Wavelet Transformation

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Pattern Recognition for Brain-Computer Interface on Disabled...

IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

作者： Bassani, Thiago Nievola, Julio Cesar PUCPR Data Mining and Machine Learning Research Group Brazil PUCPR PPGIa (Programa de Pós-Graduação em Informática) Brazil

ISBN: (纸本)9781424417780

The main objective of this work is to present an exploratory approach on electroencephalographic (EEG) signal, analyzing the patterns on the time-frequency plane. This work also aims to optimize the EEG signal analysis through the improvement of classifiers and, eventually, of the BCI performance. In this paper a novel exploratory approach for data mining on EEG signal based on continuous wavelet transformation (CWT) and wavelet coherence (WC) statistical analysis is introduced and applied. The CWT allows the signal underlying information content illustration by representing time-frequency patterns on Wavelet Coherence qualitative analysis. Results suggest that the proposed methodology is capable of identifying regions on time-frequency spectrum during the specified task on BCI. Furthermore, an example of a region is identified, and the patterns were classified using a radial basis function neural network (RBF-NN). This innovative characteristic of the process justify the feasibility of the proposed approach on another data mining applications. It can open new physiologic researches on this field and researches on different non-stationary time series analysis.

关键词： Pattern analysis Classification Signal Processing Brain Computer Interface

Toward personalized care management of patients at risk- The diabetes case study 11

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Toward personalized care management of patients at risk- The...

17th ACM SIGKDD International Conference on Knowledge Discovery and data mining, KDD 2011

作者： Neuvirth, Hani Ozery-Flato, Michal Laserson, Jonathan Rosen-Zvi, Michal Hu, Jianying Kohn, Martin S. Ebadollahi, Shahram Machine Learning and Data Mining Group IBM Research Mount Carmel Haifa 31905 Israel Healthcare Transformation Group IBM Research Hawthorne NY United States

ISBN: (纸本)9781450308137

Chronic diseases constitute the leading cause of mortality in the western world, have a major impact on the patients'quality of life, and comprise the bulk of healthcare costs. Nowadays, healthcare data management systems integrate large amounts of medical information on patients, including diagnoses, medical procedures, lab test results, and more. Sophisticated analysis methods are needed for utilizing these data to assist in patient management and to enhance treatment quality at reduced costs. In this study, we take a first step towards better disease management of diabetic patients by applying state-of-the art methods to anticipate the patient's future health condition and to identify patients at high risk. Two relevant outcome measures are explored: the need for emergency care services and the probability of the treatment producing a sub-optimal result, as defined by domain experts. By identifying the high-risk patients our prediction system can be used by healthcare providers to prepare both financially and logistically for the patient needs. To demonstrate a potential downstream application for the identified high-risk patients, we explore the association between the physician treating these patients and the treatment outcome, and propose a system that can assist healthcare providers in optimizing the match between a patient and a physician. Our work formulates the problem and examines the performance of several learning models on data from several thousands of patients. We further describe a pilot system built on the results of this analysis. We show that the risk for the two considered outcomes can be evaluated from patients'characteristics and that features of the patient-physician match improve the prediction accuracy for the treatment's success. These results suggest that personalized medicine can be valuable for high risk patients and raise interesting questions for future improvements. Copyright 2011 ACM.

关键词： Risk assessment

A combinatorial algebraic approach for the identifiability of low-rank matrix completion

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A combinatorial algebraic approach for the identifiability o...

29th International Conference on machine learning, ICML 2012

作者： Király, Franz Tomioka, Ryota Machine Learning Group Franklinstr. 28/29 10587 Berlin Germany FU Berlin Discrete Geometry Group Arnimallee 2 14195 Berlin Germany University of Tokyo Information-Theoretic Machine Learning and Data Mining Group 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan

ISBN: (纸本)9781450312851

In this paper, we review the problem of matrix completion and expose its intimate relations with algebraic geometry, combinatorics and graph theory. We present the first necessary and sufficient combinatorial conditions for matrices of arbitrary rank to be identifiable from a set of matrix entries, yielding theoretical constraints and new algorithms for the problem of matrix completion. We conclude by algorithmically evaluating the tightness of the given conditions and algorithms for practically relevant matrix sizes, showing that the algebraic-combinatorial approach can lead to improvements over state-of-the-art matrix completion methods. Copyright 2012 by the author (s)/owner (s).

关键词： Matrix algebra

Pattern recognition for brain-computer interface on disabled subjects using a wavelet transformation

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Pattern recognition for brain-computer interface on disabled...

IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Thiago Bassani Julio Cesar Nievola PUCPR Data Mining and Machine Learning Research Group Brazil Data Mining and Machine Learning Research Group PUCPR PPGIa (Programa de PósGraduação em Informática Brazil

关键词： Pattern recognition Brain computer interfaces Continuous wavelet transforms Electroencephalography Signal analysis Time frequency analysis Wavelet analysis Pattern analysis data mining Coherence

Topic Segmentation of Research Article Collections

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arXiv 2022年

作者： Çano, Erion Roth, Benjamin Digital Philology Research Group Data Mining and Machine Learning University of Vienna Austria

Collections of research article data harvested from the web have become common recently since they are important resources for experimenting on tasks such as named entity recognition, text summarization, or keyword generation. In fact, certain types of experiments require collections that are both large and topically structured, with records assigned to separate research disciplines. Unfortunately, the current collections of publicly available research articles are either small or heterogeneous and unstructured. In this work, we perform topic segmentation of a paper data collection that we crawled and produce a multitopic dataset of roughly seven million paper data records. We construct a taxonomy of topics extracted from the data records and then annotate each document with its corresponding topic from that taxonomy. As a result, it is possible to use this newly proposed dataset in two modalities: as a heterogeneous collection of documents from various disciplines or as a set of homogeneous collections, each from a single research topic. Copyright © 2022, The Authors. All rights reserved.

关键词： Taxonomies

Is the Computation of Abstract Sameness Relations Human-Like in Neural Language Models?

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arXiv 2022年

作者： Thoma, Lukas Roth, Benjamin Digital Philology Research Group Data Mining and Machine Learning University of Vienna Austria

In recent years, deep neural language models have made strong progress in various NLP tasks. This work explores one facet of the question whether state-of-the-art NLP models exhibit elementary mechanisms known from human cognition. The exploration is focused on a relatively primitive mechanism for which there is a lot of evidence from various psycholinguistic experiments with infants. The computation of "abstract sameness relations" is assumed to play an important role in human language acquisition and processing, especially in learning more complex grammar rules. In order to investigate this mechanism in BERT and other pre-trained language models (PLMs), the experiment designs from studies with infants were taken as the starting point. On this basis, we designed experimental settings in which each element from the original studies was mapped to a component of language models. Even though the task in our experiments was relatively simple, the results suggest that the cognitive faculty of computing abstract sameness relations is stronger in infants than in all investigated PLMs. Our implementation is available on https://***/lmthoma/computation_ASRs. Copyright © 2022, The Authors. All rights reserved.

关键词： Natural language processing systems

Focused contrastive training for test-based constituency analysis

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arXiv 2021年

作者： Roth, Benjamin Çano, Erion Digital Philology Research Group Data Mining and Machine Learning University of Vienna Austria

We propose a scheme for self-training of grammaticality models for constituency analysis based on linguistic tests. A pre-trained language model is fine-tuned by contrastive estimation of grammatical sentences from a corpus, and ungrammatical sentences that were perturbed by a syntactic test, a transformation that is motivated by constituency theory. We show that consistent gains can be achieved if only certain positive instances are chosen for training, depending on whether they could be the result of a test transformation. This way, the positives, and negatives exhibit similar characteristics, which makes the objective more challenging for the language model, and also allows for additional markup that indicates the position of the test application within the sentence. Copyright © 2021, The Authors. All rights reserved.

关键词： Computational linguistics

Cloud-based event-processing architecture for opinion mining

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Cloud-based event-processing architecture for opinion mining

2011 IEEE World Congress on Services, SERVICES 2011

作者： Grivas, Stella Gatziu Schaaf, Marc Kaschesky, Michael Bouchard, Guillaume Information and Knowledge Management Unit University of Applied Sciences Switzerland E-Government Unit Bern University of Applied Sciences Switzerland Data Mining and Machine Learning Group Xerox Research Center Europe Grenoble France

ISBN: (纸本)9780769544618

The viability of cloud computing for information-intensive tasks arising in real-time opinion mining and sentiment analysis of large online text streams is described. We show how a smart distributed architecture enables an efficient and scalable design for opinion mining on internet-based content that answers key challenges, such as integrating heterogeneous data sources and adapting to events through dynamic system configuration. In particular, we present a novel approach of semantic complex event processing in a cloud environment capturing different levels of information, such as event data (e.g. content from various heterogeneous, distributed sources) as well as associations identified during the opinion mining and sentiment analysis process (e.g. dynamic co-reference resolution). © 2011 IEEE.

关键词： Sentiment analysis